Animal movement expressed through home ranges or space-use can offer insights into spatial and habitat requirements. However, different classes of estimation methods are currently instinctively applied to answer home range, space-use or movement-based research questions regardless of their widely varying outputs, directly impacting conclusions. Recent technological advances in animal tracking (GPS and satellite tags), have enabled new methods to quantify animal space-use and movement pathways, but so far have primarily targeted mammal and avian species. Most reptile spatial ecology studies only make use of two older home range estimation methods: Minimum Convex Polygons (MCP) and Kernel Density Estimators (KDE), particularly with the Least Squares Cross Validation (LSCV) and reference (href) bandwidth selection algorithms. These methods are frequently applied to answer space-use and movement-based questions. Reptile movement patterns are unique (e.g., low movement frequency, long stop-over periods), prompting investigation into whether newer movement-based methods –such as dynamic Brownian Bridge Movement Models (dBBMMs)– apply to Very High Frequency (VHF) radio-telemetry tracking data. We simulated movement data for three archetypical reptile species: a highly mobile active hunter, an ambush predator with long-distance moves and long-term sheltering periods, and an ambush predator with short-distance moves and short-term sheltering periods. We compared traditionally used estimators, MCP and KDE, with dBBMMs, across eight feasible VHF field sampling regimes for reptiles, varying from one data point every four daylight hours, to once per month. Although originally designed for GPS tracking studies, dBBMMs outperformed MCPs and KDE href across all tracking regimes in accurately revealing movement pathways, with only KDE LSCV performing comparably at some higher frequency sampling regimes. However, the LSCV algorithm failed to converge with these high-frequency regimes due to high site fidelity, and was unstable across sampling regimes, making its use problematic for species exhibiting long-term sheltering behaviours. We found that dBBMMs minimized the effect of individual variation, maintained low error rates balanced between omission (false negative) and commission (false positive), and performed comparatively well even under low frequency sampling regimes (e.g., once a month). We recommend dBBMMs as a valuable alternative to MCP and KDE methods for reptile VHF telemetry data, for research questions associated with space-use and movement behaviours within the study period: they work under contemporary tracking protocols and provide more stable estimates. We demonstrate for the first time that dBBMMs can be applied confidently to low-resolution tracking data, while improving comparisons across regimes, individuals, and species.

中文翻译：

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爬行动物走错了路？使用动态布朗桥运动模型超越传统估计器

通过家庭范围或空间使用表达的动物运动可以提供对空间和栖息地要求的洞察。然而，不同类别的估计方法目前被本能地应用于回答家庭范围、空间使用或基于运动的研究问题，无论其输出差异很大，直接影响结论。动物追踪（GPS 和卫星标签）方面的最新技术进步使量化动物空间使用和移动路径的新方法成为可能，但迄今为止主要针对哺乳动物和鸟类。大多数爬行动物空间生态学研究仅使用两种较旧的家庭范围估计方法：最小凸多边形 (MCP) 和核密度估计器 (KDE)，特别是使用最小二乘交叉验证 (LSCV) 和参考 (href) 带宽选择算法。这些方法经常用于回答空间使用和基于运动的问题。爬行动物的运动模式是独特的（例如，低运动频率、较长的停留时间），促使人们研究更新的基于运动的方法——例如动态布朗桥运动模型 (dBBMM)——是否适用于甚高频 (VHF) 无线电遥测跟踪数据。我们模拟了三种典型爬行动物物种的运动数据：高度移动的活跃猎人、具有长距离移动和长期庇护期的伏击捕食者，以及具有短距离移动和短期庇护期的伏击捕食者。我们将传统使用的估计器 MCP 和 KDE 与 dBBMM 进行了比较，跨越了八种可行的 VHF 现场采样机制，适用于爬行动物，从每四个白天的一个数据点到每月一次不等。尽管最初是为 GPS 跟踪研究设计的，dBBMM 在所有跟踪方案中在准确揭示运动路径方面都优于 MCP 和 KDE href，只有 KDE LSCV 在某些更高频率的采样方案中表现相当。然而，由于高现场保真度，LSCV 算法未能与这些高频方案收敛，并且在采样方案中不稳定，这使得它对于表现出长期遮蔽行为的物种的使用存在问题。我们发现，dBBMM 将个体差异的影响降至最低，在遗漏（假阴性）和委托（假阳性）之间保持平衡的低错误率，即使在低频采样制度（例如，每月一次）下也表现相对较好。我们推荐 dBBMM 作为爬行动物 VHF 遥测数据的 MCP 和 KDE 方法的有价值替代方案，对于研究期间与空间使用和运动行为相关的研究问题：它们在当代跟踪协议下工作并提供更稳定的估计。我们首次证明，dBBMM 可以自信地应用于低分辨率跟踪数据，同时改进跨制度、个体和物种的比较。